Course

Generalized Linear Models (STA600)

Facts

Course code STA600

Credits (ECTS) 10

Semester tution start Spring

Language of instruction English

Number of semesters 1

Exam semester Spring

Time table View course schedule

Literature The syllabus can be found in Leganto

Introduction

Introduction to glm, which is a generalization of (multiple) regression for normally distributed responses to responses from a larger class of distributions, especially discrete responses. Theory for glmÂ’s with application to regression for normally distributed data, logistic regression for binary and multinomial data; Poisson regression and survival analysis. Applications to data, principles of statistical modeling, estimation and inference are emphasized. Likelihood theory.

Content

Introduction to generalized linear models (GLM), which is a generalization of (multiple) regression for normally distributed responses to responses from a larger class of distributions, especially discrete responses. Theory for GLMs with application to among other tings, regression for normally distributed data, logistic regression for binary and multinomial data; Poisson regression and survival analysis. Principles of statistical modeling, likelihood theory, estimation and inference, bayesian methods. Applications and analyses of data sets are emphasized.

Learning outcome

After having completed the course one the student should:

  • Know the main theory for generalized linear models
  • Know how regression with binary, multinomial, Poisson- and survival time responses may be done
  • Understand use of likelihood estimation generally and especially for generalized linear models
  • Be able to apply the theory in practical use on real data.

Required prerequisite knowledge

  • Mathematical Methods 1 (MAT100)
  • Mathematical Methods 2 (MAT200)
  • Probability and Statistics 1 (STA100)
  • Probability and Statistics 2 (STA500)
or
  • Mathematical Methods 1 (MAT100)
  • Linear Algebra (MAT110)
  • Probability and Statistics 1 (STA100)
  • Probability and Statistics 2 (STA500)
or
  • Mathematical Methods 1 (MAT100)
  • Mathematical Methods 2 (MAT200)
  • Probability and Statistics 1 (STA100)
  • Statistical Learning (STA530)
or
  • Mathematical Methods 1 (MAT100)
  • Linear Algebra (MAT110)
  • Probability and Statistics 1 (STA100)
  • Statistical Learning (STA530)
or equivalent courses.

Recommended prerequisites

Probability and Statistics 2 (STA500)

Exam

Oral exam

Weight 1/1

Duration 45 Minutes

Marks Letter grades

Aid None permitted

Oral exam is individual.

Coursework requirements

Two compulsory assigned exercises
Mandatory assignments must be passed for the student to have admittance to the exam.

Method of work

4 hours lectures and 2 hours problem solving per week.

Open for

Admission to Single Courses at Master Level at the Faculty of Science and Technology
City and Regional Planning - Master Computational Engineering - Master Computer Science - Master Environmental Engineering - Master Industrial Economics - Master Structural and Mechanical Engineering - Master Mathematics and Physics - Master Mathematics and Physics - Master Industrial Asset Management - Master Marine and Offshore Technology - Master Petroleum Engineering - Master

Admission requirements

Must meet the admission requirements of one of the study programmes the course is open for.

Course assessment

The faculty decides whether early dialogue will be held in all courses or in selected groups of courses. The aim is to collect student feedback for improvements during the semester. In addition, a digital course evaluation must be conducted at least every three years to gather students’ experiences.

Contact

Head of Department:
Bjørn Henrik Auestad
Course coordinator:
Jörn Schulz

Offered by

Faculty of Science and Technology

Department of Mathematics and Physics

The course description is retrieved from FS (Felles studentsystem). Version 1